SVM：加權樣本#

繪製加權資料集的決策函數，其中點的大小與其權重成正比。

樣本加權會重新調整 C 參數，這意味著分類器會更加強調正確地處理這些點。這種影響通常可能很微妙。為了強調此處的影響，我們特別加權離群值，使決策邊界的變形非常明顯。

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import matplotlib.pyplot as plt
import numpy as np

from sklearn import svm


def plot_decision_function(classifier, sample_weight, axis, title):
    # plot the decision function
    xx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500))

    Z = classifier.decision_function(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)

    # plot the line, the points, and the nearest vectors to the plane
    axis.contourf(xx, yy, Z, alpha=0.75, cmap=plt.cm.bone)
    axis.scatter(
        X[:, 0],
        X[:, 1],
        c=y,
        s=100 * sample_weight,
        alpha=0.9,
        cmap=plt.cm.bone,
        edgecolors="black",
    )

    axis.axis("off")
    axis.set_title(title)


# we create 20 points
np.random.seed(0)
X = np.r_[np.random.randn(10, 2) + [1, 1], np.random.randn(10, 2)]
y = [1] * 10 + [-1] * 10
sample_weight_last_ten = abs(np.random.randn(len(X)))
sample_weight_constant = np.ones(len(X))
# and bigger weights to some outliers
sample_weight_last_ten[15:] *= 5
sample_weight_last_ten[9] *= 15

# Fit the models.

# This model does not take into account sample weights.
clf_no_weights = svm.SVC(gamma=1)
clf_no_weights.fit(X, y)

# This other model takes into account some dedicated sample weights.
clf_weights = svm.SVC(gamma=1)
clf_weights.fit(X, y, sample_weight=sample_weight_last_ten)

fig, axes = plt.subplots(1, 2, figsize=(14, 6))
plot_decision_function(
    clf_no_weights, sample_weight_constant, axes[0], "Constant weights"
)
plot_decision_function(clf_weights, sample_weight_last_ten, axes[1], "Modified weights")

plt.show()